YAKOV KESELMAN
425.638.3968
Yakov.Keselman@gmail.com
Summary
Experienced,
highly creative applied researcher is looking for a fast-paced, collaborative
environment that will utilize his excellent problem solving and communication
skills and expertise in applied data analysis (machine learning, optimization, pattern
recognition) and in algorithm and software development.
Relevant
Experience
January
2008 – present:
Software Development Engineer, Item Data Management, Amazon.com, Seattle, WA.
- Designed
and implemented in Python/SQL a rule-based evaluator of approaches to data
reconciliation (deriving an optimal value for an attribute from multiple attribute
values).
- Designed,
implemented in Python/SQL, and validated an approach to data
reconciliation.
September
2006 – December 2007:
Software Development Engineer, SQL Server Analysis Services, Microsoft
Corporation, Redmond, WA.
- Designed,
implemented in C++, tested, and documented improved MDX query processing strategies
for the next release of Microsoft SQL Server Analysis Services.
- Debugged
and fixed several customer-reported issues related to MDX query processing.
September
2005 – August 2006:
Independent researcher specializing in applications of machine learning and
optimization techniques to practical problems in data analysis.
- Designed
and implemented in Java a subsystem aimed at integration of multiple
sources of geographical data. The subsystem was used in a larger system
that extracts useful geographical patterns from large repositories of
satellite imagery (NCSA, NASA).
- Addressed
the problem of computing an optimal representative of a cluster of graphs by
a combination of several graph-based approximation algorithms. Implemented
the approach in C++/LEDA. Validated the approach on real data.
- Reformulated
the problem of model-based clustering of image primitives as a learning
problem defined on their attributes. Applied existing machine learning
techniques to the resulting problem. Obtained preliminary clustering rules,
with subsequent validation.
July
2001 – July 2005:
Assistant Professor, School of Computer Science, Telecommunications and
Information Systems, DePaul University, Chicago, IL.
- Reformulated
the problem of image matching as a many-to-many matching problem on
attributed graphs. To address the latter problem, adapted existing graph
optimization techniques and co-developed novel approximation graph
algorithms. Co-implemented the algorithms in C++/LEDA. Validated the
approach on real data. Results were published in several leading pattern
recognition conferences and journals.
- Supervised
a Ph.D. student in designing and implementing evolutionary (genetic) algorithms
aimed at finding an optimal artificial neural network architecture for
edge detection in images. Results were presented at a student conference.
- Through
teaching and research, developed thorough understanding of practical data
management techniques, including: indexing and retrieval of textual and
image data, system performance optimization via distribution and
multithreading, data warehousing, data cleansing, data classification and
clustering, data-driven decision making.
- Encouraged
student research through projects, seminars, and career-related feedback.
- Submitted
several curriculum development proposals addressing students’ needs.
September
1994 – May 2001:
Research and Teaching Assistant, Department of Computer Science, Rutgers
University, Piscataway, NJ.
- Reformulated
the problem of image-based automated model acquisition for generic object
recognition as an optimization problem on attributed graphs. Developed
approximation algorithms for solving the formulation. Implemented the
algorithms in C++/LEDA. Results were published in a leading pattern
recognition journal.
- Lead
a team of 4 graduate and undergraduate students in the design and
development of a distributed multithreaded system for robot tracking and
control that is robust to noise and changes in the environment.
Implemented system’s GUI in Tcl/Tk and C++.
- Designed
and implemented in Matlab a system for extraction and characterization of
regions of interest in biomedical images. Validated the system on actual
data. Presented an overview of the system at a leading conference in
biomedical engineering.
- Developed
a model-based approach for tracking polyhedral objects that is robust to
noise and occlusion. Implemented the approach in Matlab and validated it
on real image sequences. Presented results at a departmental research
seminar.
- Implemented
in C a distributed version of an optimization algorithm for a linear
programming problem on the Parallel Virtual Machine architecture. The
implementation was used to obtain results for a journal publication.
List
of Publications: http://www.yashma.org/yakovkeselman/Publications/
Education
·
Ph.D.,
Computer Science, Rutgers University, Piscataway, NJ. 3.8 GPA.
May, 2005.
o
Coursework
included artificial intelligence, robust statistical estimation, databases, design
and analysis of algorithms, linear programming, randomized algorithms, numerical
analysis, network flows, image processing, and computer vision.
o
Received
Rutgers University fellowships in Digital Libraries and in Cognitive Science.
·
M.A.,
Mathematics, University of Georgia, Athens, GA. 3.8 GPA.
June, 1994.
·
B.S.,
Mathematics and Computer Science, the Urals State University, Ekaterinburg, Russia. 3.8 GPA.
June, 1991.
Visa
Status:
US citizen